arXiv (All)
open access
RAFT: Redundancy-Aware Input Filtering for Temporal Graph Neural Network Training
Temporal graph neural networks (TGNNs) have shown strong performance on dynamic graph learning tasks by continuously updating node memories from evolving interactions (i.e., events) and leveraging these memories for downstream predictions. As graphs grow rapidly in modern applications, training requ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks#1326980
- A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks#1318999
- ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs#1187306
- Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation#1117641
- Revisiting Simple Baselines for Relational Deep Learning#1355452
Record · ID 1389821
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive